Inductive knowledge graph completion
12 papers with code • 3 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Inductive knowledge graph completion
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Most implemented papers
Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
Inductive Entity Representations from Text via Link Prediction
dfdazac/blp
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However, the extent to which these representations learned for link prediction generalize to other tasks is unclear.
RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form.
DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs
alisadeghian/DRUM
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NeurIPS 2019
Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities.
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation
THU-KEG/KEPLER
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Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text.
Towards Learning Instantiated Logical Rules from Knowledge Graphs
Instantiated rules contain constants extracted from KGs.
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Many systems have been developed in recent years to mine logical rules from large-scale Knowledge Graphs (KGs), on the grounds that representing regularities as rules enables both the interpretable inference of new facts, and the explanation of known facts.
Relational Message Passing for Fully Inductive Knowledge Graph Completion
zjukg/rmpi
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Subgraph reasoning with message passing is a promising and popular solution.
InGram: Inductive Knowledge Graph Embedding via Relation Graphs
bdi-lab/ingram
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In this paper, we propose an INductive knowledge GRAph eMbedding method, InGram, that can generate embeddings of new relations as well as new entities at inference time.
Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis
anilakash/indkgc
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The task of inductive knowledge graph completion requires models to learn inference patterns from a training graph, which can then be used to make predictions on a disjoint test graph.
